All GEN AI insights Flashcards
Identify a Compelling Portfolio of Problems Across the healthcare Organization that GenAi can be of use
Improving Clinical Outcomes. AI-powered support tools enable clinicians to make more-informed decisions. For example, they can automatically review diagnostic images and flag anomalies, allowing clinicians to prioritize those that indicate the greatest potential risk to patients.
Improving the Patient Experience. GenAI-powered chatbots can explain the details of a treatment plan—in the patient’s native language and factoring in literacy level—leading to better adherence and easing the burden on clinical teams.
Improving the Provider Experience. Scribing solutions can record and transcribe patient-provider conversations and provide summaries for clinicians to review and approve for EMR documentation. This helps reduce the amount of time that clinicians spend on EMR processes—up to 40% of their day—and also helps reduce burnout in a talent-constrained market.
Managing Costs. Administrative revenue cycle management (RCM) processes can be automated and streamlined. Documentation such as patient referrals, prior authorizations, and medical coding can be autopopulated, increasing the efficiency and accuracy of submissions and augmenting the capacity of the workforce. Thoughtful AI coding and note review can reduce claims denials by 15% and the time spent capturing charges by 80%. (See “How Generative AI Improves RCM Functions.”)
Prioritize GenAi Use Cases in healthcare by
In prioritizing use cases, it’s helpful to segment across two dimensions: potential value and ease of execution.
Often it’s not possible to quantify the full value of an initiative, including new revenue, cost management, and patient value. But organizations can simplify the process and make various options broadly comparable by using a basic, five-point scoring system, with five indicating the greatest potential value and one reflecting the least.
Regarding ease of execution, it’s helpful to understand the organization’s ability to implement a GenAI application and sustain it over time; that means determining whether the necessary people, platform, and partnerships are in place
Where does much of GEN AI promise lie?
Much of AI’s promise lies in its ability to eliminate mundane or repetitive tasks from workflows
Always on GEN AI
Always-on” GenAI agents are configurable to run in parallel with human activities, delivering useful augmentation in real time. For example, an assistant can listen in on team meetings and determine when there is an information gap or anticipate and suggest next-best actions. Always-on agents are already present in tools such as Microsoft Teams and Zoom for transcription and summarization and are quickly spreading into task-specific scenarios such as customer service contact centers.
Prompt engineering issues
As opposed to the traditional button clicks of most user interfaces, engaging with GenAI tools often involves unstructured natural language text, verbal inputs, or even multi-modal inputs (a mix of voice and image, for example). These are new ways of interacting with machines—and while describing what someone wants in words may seem like a simple act, most users are not experienced in prompt engineering. Unbeknownst to them, the model can produce variable or seemingly inconsistent responses with slight word changes or model updates
What is the onboarding experience? How can users progressively advance their confidence, speed-to-output, and overall quality? And how can the GenAI tools themselves learn about their users’ needs, interests, and behaviors to deliver better experiences? For example, augmenting chat interfaces with familiar interface controls, such as buttons or filters, could help reduce mistakes and enable users to guide the GenAI toward outcomes with a much higher chance of first-time success.
What are some areas companies can assess or score a gen AI tool they are hoping to deploy?
Relevance – Are responses within the product or brand’s area of expertise?
Accuracy – Are responses correct and free from misinformation?
Brand alignment – Do responses align with the brand’s style, tone, and values?
What are some potential bias in designing AI products
User Diversity: One potential bias in designing AI products is the over-representation of certain user groups. If the design process does not adequately consider diverse user needs and perspectives, the resulting products may not serve a broad user base effectively.
Ethical Considerations: The article might not fully address the ethical implications of generative AI, such as data privacy, bias in AI algorithms, and the potential for misuse.
Technological Limitations: The analysis may overlook or understate current technological limitations of AI, leading to overly optimistic predictions about what can be achieved in user experience design.
Commercial Focus: There might be a bias towards commercial applications, potentially overlooking how generative AI could benefit public services, education, or non-profit sectors.
Future trends of GEN-AI
Enhanced Personalization: As generative AI becomes more sophisticated, we can expect a significant increase in the personalization of user experiences.
Ethical and Responsible AI Design: There will be a growing emphasis on ethical considerations in AI development. This includes addressing issues of bias, privacy, and ensuring that AI systems are transparent and accountable.
Integration of AI with Other Emerging Technologies: The integration of generative AI with other emerging technologies like augmented reality (AR), virtual reality (VR), and the Internet of Things (IoT) could lead to the creation of more immersive and interactive experiences.
Emotionally Intelligent AI: Advancements in understanding and processing human emotions through AI will lead to more emotionally intelligent interfaces that can better respond to and engage with users on a more personal level.
Collaborative AI: Future AI systems will likely focus on collaborative models where AI works alongside humans, augmenting human creativity and decision-making rather than replacing it.
Greater Regulatory and Ethical Scrutiny: As AI becomes more integrated into daily life, expect more regulatory oversight and ethical scrutiny to ensure that these technologies are being used responsibly and for the public good.
AI for Sustainability: The use of AI to address sustainability challenges, like climate change and resource management, could become a more prominent trend, aligning technology development with environmental and social goals.
